Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Statistical Machine Learning ECON5129

  • Academic Session: 2022-23
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

Machine learning is a field devoted to developing algorithms that adapt their behaviour to data, providing useful representations of the data and/or predictions. This course covers some fundamental theoretical concepts in machine learning, and common patterns for implementing methods in practice. The intended audience are those wanting the background required to begin research and development of machine learning methods.


One two-hour lecture per week for 10 weeks.

One two-hour computer lab per week for 10 weeks.

Requirements of Entry

Please refer to the current postgraduate prospectus at:

Excluded Courses






Word Count

Take home exam




Coursework 2



Max 2500 words

Main Assessment In: December

Course Aims

The main aim of this course is to introduce methods and algorithms drawn from statistical machine learning and data science, and allow students to apply these into an array of empirical problems in economics and finance. The precise set of methods and algorithms used to illustrate and explore the main concepts will change slightly from year to year. However, the main topic headings are expected to be fairly stable.

Intended Learning Outcomes of Course

On completion of this course, the student will be able to:

1. Structure an applied problem as a machine learning task, identifying appropriate methods.

2. Criticise and validate alternative machine learning methods for a given task.

3. Devise and motivate novel variants of machine learning methods.

4. Write accessible and useful explanations of the workings and failure modes of machine learning methods.

5. Program and refine implementations of learning algorithms, while applying them in practice.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.